To meet the higher requirements for hospital management imposed by national policies on medical consumables, such as “zero markup” and “centralized volume-based procurement”, the supply-processing-distribution (SPD) management model for medical consumables has been gradually promoted. However, there is currently a lack of unified management standards, and the models are developing in a diversified manner. Focusing on large general hospitals, this study first sorted out and compared the operational characteristics, advantages and disadvantages of the three major mainstream SPD models, namely the hospital-owned and self-operated model, the centralized distribution service model, and the hospital-led operation outsourcing model. Secondly, it analyzed the development trend of the SPD model nationwide. Finally, it summarized the current innovation directions of the models, including the introduction of a third-party SPD pure service model with no interest ties, and the enhancement of hospitals’ management initiative through the combination and collaboration of multiple service providers. This study aims to identify the limitation that existing research on the SPD model is mostly qualitative description, and proposes that subsequent research should strengthen the quantitative comparison in dimensions such as supply chain efficiency and management quality. Meanwhile, it provides references for large general hospitals (as the core carriers of SPD model innovation), helping them analyze the advantages and disadvantages of the models based on actual data, promote the implementation of SPD management standards, and improve the management efficiency of the medical consumables supply chain.

Objective To design a multi-supplier supply-processing-distribution (SPD) management platform to solve the problem of multi-supplier management in hospital consumables management and optimize the full-process management. Methods Adopt the model of “self-built platform and connection with supplier systems”, through multi-level system architecture design, build a unified management process, data standards and assessment system, achieve seamless connection with the hospital system, cover the links of access, distribution, use and settlement, and be equipped with intelligent devices and analysis tools. Results After the system was put into use, the accuracy rate of delivery, the timeliness rate of delivery and the number of delivery orders were all improved compared with those before the system was used, and the differences were statistically significant (P<0.05). Meanwhile, the number of service personnel required by the hospital decreased from 13 to 9, and the number of orders processed per person increased, which saved labor costs and improved service efficiency. Conclusion The SPD platform enhances the refined level of consumable management, forms a management closed loop, and significantly improves the operational efficiency and medical quality of the hospital. In the future, the system will continue to be optimized, the application of radio frequency identification technology will be expanded, and the management accuracy and efficiency will be enhanced.

Objective To explore the reform of supply-processing-distribution (SPD) supply chain services for medical consumables in hospitals, facilitate the refined management of medical consumables, and strengthen the refined management practice in all circulation links of medical consumables through data integration. Methods Data interfaces were connected to link information systems such as the hospital information system (HIS), hospital resource planning (HRP), and electronic medical record (EMR). A medical consumable supply management system was constructed, covering functions including consumable coding, inventory management, clinical traceability, intelligent replenishment, and settlement connection. Through the monitoring and analysis of logistics data in various usage links, the whole-life-cycle data traceability management of medical consumable supply was realized. Results After the launch of the SPD model, more than 130000 surgical procedures were supported and over 980000 pieces of consumables were distributed in 2024. Compared with the traditional model, the SPD model shortened the preoperative preparation time, consumable retrieval time, and settlement & billing time by 84.96%, 24.00%, and 62.50% respectively (P all<0.001). In terms of cost and management optimization, zero-inventory management of consumables was achieved; 178 out of 1101 consumable specifications completed price adjustments, resulting in an annual cost saving of 3.3 million yuan. The cost of surgical consumables in 2024 was significantly lower than that in 2023 (P<0.001). Conclusion The SPD reform meets the needs of refined management of medical consumables, and realizes the full-chain closed-loop management of “clinical-logistics-finance” for the first time. It has application integration and clinical adaptability, can improve management efficiency, and holds practical significance and promotion value for the high-quality development of medical institutions.

Objective To address the current management status of non-sterile orthopedic consumables, reshape the workflow, achieve full-process supervision, effective traceability, unified usage and billing, improve billing accuracy and efficiency, and reduce sterilization costs for non-sterile consumables. Methods A digital orthopedic management platform was established using digital technology, implementing a “one-item-one-code” management model for non-sterile orthopedic consumables. An advanced image recognition system was introduced to streamline the management process, extending supervision to the hospital’s internal warehousing stage. This enabled precise acceptance of each implant, internal traceability, and accurate billing. The effectiveness of the new model was evaluated by comparing billing accuracy rates, billing duration, and sterilization costs before and after the system’s implementation. Results After the new model was implemented, billing accuracy increased from 99.95% to 100%; billing duration decreased by 62.5%; and sterilization costs decreased by 87.5% compared to pre-implementation levels, with all differences being statistically significant (P<0.05). Conclusion The implementation of the “one-item-one-code” management model for non-sterile orthopedic consumables not only enhances the efficiency and accuracy of consumable management but also reduces hospital operational costs, demonstrating significant practical value and potential for broader application.

Objective To explore the application practice and specific effects of the multi-party collaborative kit preparation and management model for surgical medical consumables based on the supply-processing-distribution (SPD) information platform in a large public Grade-III hospital. Methods Data from September 2023 (control group) and September 2024 (observation group) in the hospital were selected. The two groups were compared in terms of total daily preoperative consumable preparation time, preoperative consumable preparation error rate, intraoperative consumable acquisition time per operation, consumable billing time per operation, postoperative inventory time per operation, and surgical billing error rate. The effects before and after the application of the model were analyzed. Results After the public Grade-III hospital applied the multi-party collaborative kit preparation and management model for surgical medical consumables based on the SPD information platform, the total daily preoperative consumable preparation time, preoperative consumable preparation error rate, intraoperative consumable acquisition time per operation, consumable billing time per operation, postoperative inventory time per operation, and surgical billing error rate all decreased significantly, with statistically significant differences (P<0.05). Conclusion The collaborative preparation management mode of surgical medical apparatus based on SPD information platform is a high-efficiency, high-accuracy, and high-management effectiveness management mode of surgical medical apparatus. It can effectively improve the level of efficient and refined management of surgical medical consumables in public hospitals.

Objective To design an integrated system for real-time dielectric property measurement of biological tissues based on the initial coaxial probe method, analyze the accuracy of the system in measuring the dielectric properties of tissues, and study the influence of probe pressure on the measurement of dielectric properties. Methods Based on components such as single-chip microcomputers, pressure sensors, and signal processing circuits, a reflection coefficient and probe pressure measurement device was constructed. The upper computer software with calibration and dielectric properties calculation functions was developed to display the probe pressure and dielectric property measurement tissue in real time. After the measurement system was established, the dielectric properties of methanol, n-propanol, liver phantom, glioma and normal brain tissue were measured, and the dielectric properties of the tissues measured by this system were verified based on standard parameters such as the coefficient of dispersion, average percentage error and relative difference. Results The average dispersion coefficients of the relative dielectric constant and electrical conductivity of methanol within the sweep frequency range were 0.0026 and 0.0827, respectively. The average percentage errors were 0.85% and 16.01%, respectively. The average dispersion coefficients of the relative dielectric constant and electrical conductivity of n-propanol within the sweep frequency range were 0.0028 and 0.0078, respectively. The average percentage errors were 2.43% and 14.38%, respectively. The relative differences in dielectric constants between glioma tissue and normal brain tissue at frequencies of 64, 128 and 298 MHz were 57.28%, 48.97% and 48.85%, respectively. The relative differences in electrical conductivity were 73.61%, 69.9% and 66.26%, respectively. Conclusion This system can be used for real-time in vitro measurement of dielectric properties of biological tissues, and at the same time provides a reference for studying the influence of probe pressure on the measurement of dielectric properties.

Objective To design a hand key point detection algorithm for the intelligent guidance and evaluation system of Baduanjin, in order to solve a series of challenging problems such as occlusion, complex action details and mobile deployment. Methods Based on the YOLOv8n-pose model, a hand key point detection algorithm YOLO-BDJ-Hands for the Baduanjin scene was proposed. The separation enhanced attention module was introduced to learn the adaptive weight attention of hand occlusion region. The receptive field expansion module was used to increase the receptive field range and enhance the ability to detect hand detail features at different scales. Results On the Baduanjin hand key point dataset, the detection precision and recall rate of the YOLO-BDJ-Hands algorithm were 0.912 and 0.901 respectively. The number of model params was 3.1 M, which was 9% less than that of the original model. The floating point operations was 9.5 B, which could meet the requirements of mobile deployment. Conclusion The YOLO-BDJ-Hands algorithm proposed in this study has high robustness and generalization performance, basically achieving model lightweighting, facilitating deployment and application on mobile terminals, and can provide effective technical support for the subsequent development and promotion of the intelligent guidance and evaluation system of Baduanjin.

Objective To explore a cross-perspective strategy for aligning medical images and medical reports to optimize the quality of medical image reports automatically generated by deep learning models. Methods A generative model combining cross-view features and attention mechanism was designed. Firstly, different angles were taken by the medical image and the pathological description features of the report were encoded based on the pre-trained model. Then, multiple attention mechanisms were utilized to complete the fusion calculation of features. Finally, the joint features were decoded into case reports using the decoder. Results It could be known from multiple rounds of tests on the authoritative and publicly available chest X-Ray dataset IU X-ray that the average performance of this model in the evaluation metrics of BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROUGE_L and P_MEAN was higher than that of the previously proposed methods by 8.34%, 14.20%, 10.90%, 6.14%, 1.70% and 5.50% respectively, and the comprehensive performance was improved by 7.79%. Conclusion This model performs well in terms of the accuracy and fluency of report generation, indicating that this fusion strategy can better capture the potential connection between images and reports, and improve the performance of the model in generating reports.

Objective To address the common problems faced in medical image segmentation, such as complexity, blurred tissue boundaries, and low organ contrast, especially with the wide application of the Transformer self-attention mechanism in medical image segmentation, it has the pain point of poor segmentation effect. Methods Multi-scale attention aggregation and Mamba-Like linear attention mechanism were fused to construct a U-shaped network MM-UNet. Among them, the Mamba-Like linear attention mechanism was introduced to enhance the model’s learning ability for global dependencies. At the same time, a multi-scale attention aggregation mechanism was adopted. Through multi-scale convolution, key features could be captured and utilized in a targeted manner, and multi-scale semantic information could be captured more comprehensively to improve the accuracy of the model in image edge segmentation. Results Algorithms such as MM-UNet, UNet, VM-UNet, and LightM-UNet were subjected to comparative experiments on multiple datasets including dermoscopy ISIC18, ISIC17, and ultrasound thyroid nodule TN3K. The results showed that the mIoU of MM-UNet on the dermoscopy ISIC18, ISIC17 and ultrasound thyroid TN3K datasets were 83.22%, 83.63% and 80.19% respectively, and the Dice coefficients were 90.84%, 91.08% and 89.00% respectively. The accuracies were 95.86%, 96.57%, and 97.38% respectively, and the specificities were 97.95%, 98.36%, and 98.70% respectively. Among them, compared with models such as UNet, VM-UNet, and LightM-UNet, the mIoU, Dice coefficient and accuracy of MM-UNet all performed the best. The specificity of MM-UNet performed best on the ISIC18 and TN3K datasets. Conclusion MM-UNet achieves high segmentation accuracy on various medical image data with different imaging backgrounds, and its generalization ability is also good, which has certain application value.

Objective To promote the research progress of multi-modal large model of medical image and enhance the availability and accuracy of data by automatically splitting the content of medical image report. Methods The data of 39499 X-ray image reports from Anhui image cloud platform were retrospectively collected, covering 25 examination sites. A multi-label text classification method was applied to the fine-tuned pre-trained Chinese-Roberta-WWM-ext model and Chinese-BERT-WWM model. In order to verify the performance of the optimal model in the report splitting task, 200 image reports were selected for testing, and the experimental results were quantitatively evaluated by using Likert scale. Results In the fine-tuning process, Chinese-Roberta-WWMext model performed the best, and the average area under the curve of its inspection observation and inspection impression model on 25 labels were 0.982 and 0.989, respectively. The average F1 scores of Chinese-Roberta-WWM-ext model on 25 labels were 78.59% and 82.31%, respectively, for different pre-trained BERT models with a threshold value of 0.5. For 200 test image reports, the model split correctly 89% of the time. Conclusion The image report governance method based on BERT model proposed in this study provides an effective means to obtain large-scale image-text alignment data sets.

Objective To solve the problems of incomplete early warning of patients’ conditions and long early warning time existing in the current automatic early warning model of electronic medical records. Methods The relevant information of patient medical records in different medical information systems was collected, and the information was preprocessed. Then natural language processing technology was used to identify the categories and names of entities in the information, extract entity relationships and attributes, and construct the knowledge graph of electronic medical records. In the constructed knowledge graph, the trigger conditions of early warning were defined, and the association between early warning information and related entities was monitored in real time by using the structure of the knowledge graph for automatic early warning. Results The knowledge graph constructed in this study has an average reciprocal ranking greater than 0.9, which could fully embed the information in the data into the knowledge graph. The early warning accuracy of the automatic early warning model based on this knowledge graph for patients’ physical conditions, adverse reactions and unreasonable drug use was 93.5%, 92.7% and 91.9% respectively, and the early warning time was less than 2.0 s. Furthermore, after applying this early warning method, the risk of disease deterioration for patients dropped to 3.2%. Conclusion The automatic early warning method for electronic medical records proposed in this study can significantly improve the diagnosis and treatment level of hospitals.

Objective To construct an integrated specialized asthma platform that incorporates an asthma-specific database, data query functionality, and artificial intelligence analysis, thereby providing support for the standardized clinical diagnosis and treatment of asthma. Methods Data warehouse technology was used to collect diagnostic and treatment data from various hospital systems, standardized management of text and image data was implemented to establish a specialized database for pediatric asthma, and a specialized application platform for pediatric asthma was developed to accommodate the clinical management of patients and the requirements of scientific research. Results The asthma specific platform covered over 2.31 million data of 115032 children with asthma, and has incubated 7 retrospective or prospective clinical research projects. Compared with the platform went online before, the efficiency of case screening, case data collection, data cleaning, and data analysis has significantly improved, and the difference is statistically significant (P<0.05). The medication adherence rate has increased to 72.2%, the follow-up rate has increased to 74.50%, the asthma attack rate has decreased to 22.7%. The overall diagnosis and treatment level has been improved, and the difference is statistically significant (P<0.05). Conclusion The children’s asthma specialized disease platform effectively improves the management of asthma patients, enhances diagnosis and treatment efficiency. At the same time, the standardized asthma data boosts the scientific research on asthma.

Objective To design a complex disease research platform based on microservice architecture and explore its application value in precision medicine. Methods The Spring Cloud Alibaba microservice architecture was adopted to encapsulate complex disease-related data and analysis processes into public microservices and private microservices, and realize modules such as transcriptome data analysis and epigenetic data analysis. Results After the application of the scientific research platform, the project initiation time was reduced by 52.6% compared with the traditional method, and the differences were statistically significant (P<0.001). The project completion rate increased from 70% (14/20) to 90% (16/18). Conclusion The scientific research platform based on microservice architecture can be applied to clinical and basic research on complex diseases and has reference and promotion value for the development of precision medicine.

Objective To continuously improve the quality management of infant incubators in the neonatology department, and enhance the qualified rate and guarantee capability of infant incubators by PDCA cycle method. Methods Relevant data were collected, and problems and causes were analyzed by fishbone diagram and Pareto chart. Combined with PDCA cycle management, improvement measures were implemented by improving the medical equipment management system, detailing maintenance items, strengthening personnel training and assessment, and enhancing supervision. The improvement effect was evaluated based on the overall qualified rate and detailed detection parameters before and after introducing the PDCA cycle method. Results After implementing the improvement measures, the qualified rate of infant incubators increased from 87.1% to 97.1%, with a statistically significant difference (P<0.05). Meanwhile, the deviations in temperature-related indicators in the test items were significantly reduced, among which the deviations, uniformity, and fluctuation of temperature indicators showed significant changes (P<0.05). The results indicated that the PDCA cycle method improved the quality management level and performance of infant incubators. Conclusion Using the PDCA cycle method to improve the qualified rate and performance of infant incubators can promote the standardization of their quality management, providing a reference for other medical equipment in carrying out quality management work.

Objective To focus on the risk management of medical equipment near the end of its service life, identify the core influencing factors of adverse events, sort out the current management status and prominent problems, construct targeted strategies and personalized maintenance plans, and provide references for medical institutions to improve their management level. Methods The data were obtained from the adverse event cases of active medical devices in Shandong Province (2021-2023) retrieved from the National Medical Device Adverse Event Monitoring Information System. The criterion for defining medical equipment near the end of its service life was “service time exceeding 5 years or more than half of its specified service life”. Three types of equipment were selected, namely Whole-Body X-Ray Computed Tomography System (hereinafter referred to as “CT”), monitors, and anesthetic machines. After excluding invalid cases, 4982 valid samples were obtained. A multi-dimensional statistical analysis combined with the brainstorming method was adopted to analyze the basic information, characteristics of adverse events, and risk factors, and descriptive statistics were used for data processing. Results The reports of adverse events were mainly from general hospitals (accounting for 83.74%), and the number of reports increased year by year from 2021 to 2023. The overall proportion of adverse events related to domestic equipment (85.09%) was higher than that of imported equipment; however, among imported equipment, the proportion of adverse events related to CT reached 68.94%. Among the adverse events of medical equipment near the end of its service life, “serious injury” accounted for 10.68%. The three types of equipment had distinct fault characteristics, and the main cause of risks was human management omissions (inadequate preventive maintenance accounted for a relatively high proportion). Conclusion By identifying the core influencing factors of risk management for medical equipment near the end of its service life, this study systematically sorts out the core problem of “human management absence” and practical situations such as “lack of classified management thinking” in the management of such equipment in medical institutions. Furthermore, it clarifies that targeted risk management strategies and personalized preventive maintenance plans can be constructed, which provides practical references and decision support for medical institutions to improve the risk management level of medical equipment near the end of its service life.

Objective To construct a comprehensive performance evaluation model for large medical equipment in hospitals, develop an evaluation method applicable to both children’s hospitals and general hospitals, and provide references for the management and evaluation of large medical equipment in public hospitals. Methods The expert consultation method was used to build a comprehensive performance evaluation index system for large medical equipment. The index weights were calculated based on the fuzzy analytic hierarchy process. The data analysis method was used to study the diagnosis and treatment duration of magnetic resonance imaging for children of different age groups, so as to quantify the impact degree of the particularities of pediatric diagnosis and treatment. Results The constructed comprehensive performance evaluation index system for large medical equipment included 3 firstlevel indicators (economic benefits, social benefits, and scientific research value) and 13 second-level indicators. The second-level indicators with higher weights included business income, dynamic payback period, and number of services. Compared with the control group (adolescent group), the average examination duration of preschool children increased by 16.32%, and that of school-age children increased by 8.45%. The examination duration of sedated children was basically the same as that of the control group, but an additional workload of 83.1 s for medical staff was required for sedation-related matters. Conclusion The comprehensive performance evaluation model for large medical equipment based on the particularities of pediatric diagnosis and treatment provides a refined equipment management and evaluation method for children’s hospitals. This method can accurately identify the current advantages and shortcomings of large medical equipment, help hospitals improve the efficiency of equipment allocation and utilization, reduce patients’ waiting time for examinations, and enhance patient satisfaction and medical experience.

Objective To improve the utilization rate of large medical equipment and enhance the refined, digital, intelligent, and scientific management of medical equipment by constructing an evaluation system for the utilization efficiency of large medical equipment. Methods An index pool for evaluating the utilization efficiency of large medical equipment was established through literature analysis and focus group interviews. The Delphi method and analytic hierarchy process were used to determine the evaluation index system and the weight of each level of indexes. Results The finally constructed evaluation system included 4 first-level indexes (social benefits, economic benefits, management benefits, and scientific and educational benefits) and 17 second-level indexes. The second-level indexes were further divided into efficiency indexes and effectiveness indexes according to their attributes. The weights of the first-level indexes were 43.93%, 31.07%, 14.64%, and 10.36% in sequence. The weight range of the second-level indexes was 0.82%-19.90%, among which the equipment utilization rate (19.90%), return on investment (14.08%), and number of equipment-based diagnoses and treatments (11.49%) had relatively higher weights. Conclusion This index system meets the multidimensional evaluation needs and can objectively reflect the utilization efficiency of large medical equipment in public hospitals. It not only makes up for the deficiency of “emphasizing procurement over management” in traditional evaluation but also provides scientific data support for equipment procurement decisions, daily use supervision, and operation and maintenance optimization. In the future, further exploration can be conducted on its applicability in hospitals of different scales and regions, and optimization and improvement can be made in light of actual needs.

Objective To explore the management of ultrasound equipment operation efficiency based on the DEA-RSR model, evaluate the operation efficiency of ultrasound equipment in Huzhou Maternal and Child Health Care Hospital from 2021 to 2023, and provide a scientific basis for equipment management and resource allocation. Methods Taking the data of ultrasound equipment in 12 quarters as samples, “number of medical technicians, maintenance costs, and number of equipment” were selected as input indicators, and “number of diagnoses and total income” as output indicators. The data envelopment analysis (DEA) model was used to measure efficiency and identify problems of input redundancy and output insufficiency; the rank sum ratio (RSR) comprehensive evaluation method was combined to conduct comprehensive ranking and grading of efficiency in each quarter. Results DEA analysis showed that the average comprehensive efficiency of ultrasound equipment in 12 quarters was 0.944. Among them, the resource allocation in 6 quarters reached the DEA strong efficiency level, while the other 6 quarters showed non-DEA efficiency, indicating the existence of input redundancy and output insufficiency. RSR divided the operation efficiency of ultrasound equipment into three grades: “poor”, “medium” and “good”, including 1 quarter of poor grade, 9 quarters of medium grade, and 2 quarters of good grade. Moreover, RSR was highly positively correlated with the Probit value. Conclusion The DEA-RSR model has certain feasibility in optimizing the operation efficiency of equipment. Through accurate evaluation of equipment operation efficiency, it can provide targeted recommendations for resource allocation and optimization to hospital managers, thereby improving equipment utilization and service quality.

Objective To strengthen the refined management of large medical equipment in public hospitals, optimize equipment allocation and utilization efficiency, this study aims to construct a performance evaluation system for large medical equipment to analyze and evaluate the compliance of equipment allocation and utilization benefits. Methods Guided by the hospital’s strategic goals and based on the balanced scorecard theory, the evaluation dimensions were determined. Candidate indicators were screened through bibliometric statistics, and the final indicators and their weights were confirmed using a questionnaire survey method. A benchmarking assignment method was adopted to formulate scoring criteria, thereby establishing a comprehensive performance evaluation system. Results The final evaluation system consists of 5 first-level indicators (economic benefits, social benefits, allocation compliance, operation status, and scientific research benefits) and 13 second-level indicators. This system can fully reflect the allocation status, operation status, and benefit outcomes of large medical equipment, cover the entire life cycle of the equipment, and support multi-dimensional comprehensive qualitative and quantitative analysis. Taking a linear accelerator in a hospital as an empirical object, its comprehensive performance scores from 2021 to 2023 were 49.37, 43.91, and 56.41 respectively (all below 60 points). The study identified problems such as unused high-end functions and small-scale scientific research of the equipment, and proposed improvement measures accordingly. Conclusion Empirical research confirms that the performance evaluation system for large medical equipment based on the balanced scorecard is scientific, feasible, and effective. This system can provide a basis for equipment procurement decisions and full-life-cycle management, and has important theoretical value and practical significance for improving the utilization efficiency of equipment and funds. Future research needs to further optimize the indicators and scoring criteria in combination with practical applications.

Objective To explore the impact of adding 6 types of instrument package extended attributes on work efficiency and management quality based on the application of the existing disinfection supply management system. Methods A retrospective analysis was conducted on 1000 surgical instrument packages disposed of daily from January to June 2023 (control group) and 1000 surgical instrument packages disposed of daily from July to December 2023 (observation group). The control group was managed according to the conventional management system, while the observation group was managed with 6 additional types of instrument package attributes. The improvement of work efficiency and management quality before and after the adoption of instrument package extended attributes was compared. Results Compared with control group, after the extended application of 6 types of instrument package attributes, the time for executing the instrument package processing procedure was significantly shortened, the procedure allocation efficiency increased by 35.41%, the accuracy rate of instrument package handover reached 99.97%, the screening and determination efficiency of processing positions increased by about 3 times, and the accuracy rate of cost accounting with departments and other hospitals increased by 24.10%. Other indicators were also close to or reached the target values. Conclusion The use of 6 newly added instrument package attributes improves the business process and operation specifications of the Central Sterile Supply Department (CSSD), enhances the rigor and standardization of staff, ensures the processing quality of each link and the accuracy of business data, significantly improves the processing efficiency of surgical instruments, and effectively enhances the management efficiency and level of CSSD, with remarkable application effects.

Objective To explore the clinical value of dual-flow injection technique combined with low tube voltage scanning based on deep learning reconstruction algorithm in whole aortic computed tomography angiography (CTA) imaging. Methods A total of 120 patients undergoing whole aorta CTA examination were enrolled and divided into 3 groups (40 cases each) according to the injection method and scanning protocol: Group A (conventional contrast agent injection combined with 100 kV conventional scanning), Group B (dual-flow injection combined with 100 kV conventional scanning), and Group C (dual-flow injection combined with 60 kV low tube voltage scanning). Images of Groups A and B were reconstructed using a conventional iterative algorithm. Group C was further subdivided into Subgroup C1 (conventional iterative algorithm) and Subgroup C2 (deep learning algorithm) based on the reconstruction algorithm. The differences in contrast agent dosage, radiation dose, and image quality-related indicators were compared among all groups. Results The contrast agent dosage in Groups B and C was reduced by 45% compared with Group A. The effective radiation dose in Group C was significantly lower than that in Groups A and B, with a statistically significant difference (P<0.05). In terms of objective evaluation of image quality, the CT values of all aortic segments in Group C (Subgroups C1 and C2) were higher than those in Groups A and B, and the CT values in Group A were higher than those in Group B, with statistically significant differences in all comparisons (P<0.05); The standard deviation (SD) value in Subgroup C1 was higher than that in Groups A, B, and Subgroup C2, with a statistically significant difference (P<0.05), while no statistically significant difference was observed in SD values between Groups A and B (P>0.05); The signal to noise ratio (SNR) and contrast to noise ratio (CNR) at the openings of the left/right common iliac arteries in Subgroup C2 were higher than those in Group A, with statistically significant differences (P<0.05), but no statistically significant differences in SNR and CNR were found in other segments (P>0.05). Additionally, the SNR and CNR in Group A were higher than those in Group B and Subgroup C1, with statistically significant differences (P<0.05), while no statistically significant differences were noted between Group B and Subgroup C1 (P>0.05). For subjective evaluation of image quality, no statistically significant difference was found between the scores of Group A and Subgroup C2 (P>0.05), and both scores were higher than those of Group B and Subgroup C1, with statistically significant differences (P<0.05). Conclusion In whole aortic CTA, the application of dual-flow injection technique combined with low tube voltage scanning based on deep learning reconstruction algorithm can significantly reduce contrast agent dosage and radiation dose without compromising image quality, which holds important clinical value.

Objective To explore the predictors of histopathological grade in breast invasive ductal carcinoma (BIDC) and construct a histopathological grade prediction model based on contrast-enhanced ultrasound indices in the non-parenchymatous area of the tumor. Methods A total of 161 breast cancer patients with 193 tumors who underwent surgical treatment in Tongchuan People’s Hospital (South Hospital) from January 2019 to January 2024 were retrospectively included. They were divided into three groups according to postoperative histopathological grade: Grade Ⅰ (n=46), Grade Ⅱ (n=58), and Grade Ⅲ (n=89). Predictors were screened via univariate and ordinal logistic multivariate regression analyses, and the predictive performance was evaluated using receiver operating characteristic (ROC) curves. Results Univariate analysis showed that the gradient value of peak time, gradient value of peak intensity, and gradient value of area under the curve (AUC) of contrast-enhanced ultrasound in the non-parenchymatous area of the tumor were correlated with histopathological grade (P<0.05). Multivariate analysis further confirmed that these three indices were all independent predictors of histopathological grade in BIDC (P<0.05). ROC curves were constructed based on the independent influencing factors of the logistic regression model and the predicted probability derived from the model’s P-value. The results showed that the AUC values of the gradient value of peak time, gradient value of peak intensity, gradient value of AUC, and the predicted probability of the model’s P-value were 0.962, 0.685, 0.882, and 0.985, respectively. Among them, the predicted probability of the model’s P-value exhibited the optimal sensitivity (95.96%), Youden’s index (92.00%). Conclusion The gradient value of peak time, gradient value of peak intensity, and gradient value of AUC of contrast-enhanced ultrasound in the non-parenchymatous area of the tumor can be used to predict the histopathological grade of BIDC. The prediction model constructed based on these three indices exhibits excellent performance, providing an important basis for the clinical non-invasive evaluation of the invasion degree of this cancer and the formulation of diagnosis and treatment plans.

Regular blood pressure monitoring is of great significance for populations at high risk of hypertension. Existing contactbased detection technologies are associated with invasiveness and are not applicable to special groups such as patients with burns and scalds, thus creating an urgent demand for non-contact detection methods. Biomedical radar enables non-contact detection of subtle movements on the body surface to extract pulse waves for blood pressure calculation. Non-contact blood pressure detection technologies based on biomedical radar are mainly divided into three categories: those based on pulse wave velocity, those based on multiple characteristic parameters of pulse waves, and those based on deep learning. This paper reviewed the principle of noncontact blood pressure detection using biomedical radar and the research progress of these three core technologies. It also analyzed the shortcomings of each technology in terms of adaptation to individual differences, data coverage, real-time performance, and other aspects. The aim is to identify technical bottlenecks, point out future development directions, provide references for the clinical transformation of this technology, and help improve the convenience and universality of blood pressure monitoring.

As a crucial tool for optimizing end-of-life medical decision-making, advance care planning (ACP) has garnered widespread attention from healthcare systems worldwide. By proactively communicating patients’ medical preferences with the patients themselves and their families, ACP ensures that medical decisions align with individuals’ wishes, reduces unnecessary overtreatment, and enhances the quality of life during the end-of-life stage. In recent years, electronic health record (EHR) systems have been integrated into ACP to provide standardized functions for information identification, storage, sharing, and reminders. This integration aims to increase the implementation rate of ACP and improve the quality of end-of-life care. Currently, the promotion of ACP, both domestically and internationally, still faces numerous challenges, such as difficulties in determining the optimal timing for ACP discussions, the lack of standardized procedures, and inadequate mechanisms for updating patients’ preferences. Against this backdrop, this paper reviewed the current application status of EHR in ACP, analyzed domestic and international research progress as well as existing problems, and explored feasible pathways for the in-depth integration of EHR and ACP. The purpose of this paper is to promote the electronic management of ACP, improve the quality of medical decision-making, facilitate the informatization development of palliative care, and ultimately establish a more humanized, patient-centered healthcare system, while raising societal awareness and attention towards end-of-life medical planning.

Low treatment adherence among patients with hypertension has resulted in generally low blood pressure control rates in China. Mobile health (mHealth) technology enables efficient information exchange between healthcare providers and patients through methods such as mobile applications, wearable devices, and telemedicine, which can significantly improve the management efficiency of hypertension patients. With the widespread popularity of smartphones, the conditions for applying mHealth technology in health management for chronic diseases have become mature. In recent years, an increasing number of studies on the application of mHealth technology in the health management of hypertension patients have been reported. This article systematically reviewed the current application status of mHealth technology in the health management of hypertension patients, meanwhile pointed out the existing problems in the current application of this technology, and prospects its future research directions, aims to provide references for general practitioners to apply mHealth technology in the practice of hypertension health management.

The maxillary bone is less dense than the mandible. The loss of maxillary anterior teeth often leads to alveolar ridge resorption, which in turn results in insufficient bone volume in the maxillary anterior tooth area. The loss of maxillary posterior teeth is usually accompanied by maxillary sinus pneumatization, causing insufficient vertical bone volume in the edentulous area. For patients with severe maxillary bone defect who are not eligible for traditional prosthetic methods or free-hand implant surgery, achieving high-quality occlusal reconstruction through implant prosthodontics has become an urgent clinical challenge for dentists in implant surgeries for insufficient maxillary bone volume. With the advancement of oral implant technology, implant surgery guided by dynamic navigation systems has emerged as a research hotspot in the field of implantology. This paper reviewed the key applications and advantages of dynamic navigation systems in oral implant surgeries for insufficient maxillary bone volume, aiming to provide a reference for the clinical application of this technology in implant dentistry.

With the continuous advancement of communication technologies, the rapid development of the Internet of Things is profoundly transforming the healthcare sector, particularly in terms of medical device interconnection, which demonstrates significant potential. The establishment of an Internet of Things for medical devices enhances the efficiency of information flow and integration within hospitals, facilitates real-time collection of patient health data and medical environment parameters, and provides scientific evidence for clinical practice through in-depth data analysis. This further optimizes diagnosis and treatment processes and improves the quality and efficiency of medical services. Internet of Things-based medical device interconnection promotes the refined management of medical equipment by enabling real-time monitoring, intelligent scheduling, and efficient utilization, thereby substantially reducing hospital operating costs. It also plays a pivotal role in the construction of smart hospitals. Therefore, this paper has expounded the value of the construction of the Internet of Things for medical equipment, analyzed and compared the application status of medical equipment interconnection modes based on the Internet of Things, and discussed the development direction of medical equipment interconnection under Internet of Things technology. It is intended to provide forward-looking thinking and strategic suggestions for the development of smart healthcare, and promote the wide application and in-depth integration of medical equipment Internet of Things technology in the medical industry.

Cervical cancer is a tumor with a well-defined etiology that is potentially curable, and its main treatment modalities include surgery, radiotherapy, and chemotherapy. How to reduce the radiation dose to the bladder and improve patients’ local control rate during external beam radiotherapy (EBRT) is an urgent issue to be addressed in the implementation of precision radiotherapy. Radiotherapy-related complications of cervical cancer are mainly dominated by radiation cystitis, radiation proctitis, and small intestinal injury. Changes in bladder status exert a significant impact on the dose delivered to organs at risk; therefore, whole-process bladder protection is of great importance. This article systematically reviewed the key strategies for bladder protection in EBRT for cervical cancer, and analyzed the topic around three core phases: radiotherapy localization, treatment planning design, and treatment plan delivery. It aims to provide references for the clinical development of bladder protection protocols in EBRT for cervical cancer, achieve a balance between precise target volume irradiation and bladder protection, and ultimately improve patients’ treatment prognosis.

This paper analyzed the control system principle of the radiotherapy simulator (HMD-IB type) of the Second People’s Hospital of Neijiang and the working principle of the programmable logic controller (PLC). Combined with the repair and handling of four typical faults of the motion system based on PLC control, the application of maintenance methods and techniques in the process of eliminating the faults of this equipment was discussed. It summarized the importance of carefully observing the fault phenomenon, having a clear analytical approach, accurately judging the fault, and quickly restoring the normal operation of the equipment during the process of repairing faults. It has achieved the goal of not only saving maintenance time and costs but also ensuring the smooth progress of radiotherapy work in the department, providing reference opinions for peer engineers when maintaining faults in the motion system controlled by PLC.